MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company uses SageMaker Model Monitor for data quality. They notice that monitoring jobs are failing intermittently with constraint violations. Upon review, they see that some features have different data types in production compared to the baseline (e.g., string instead of integer). Which type of drift is this?
⚠ Common exam trap
Many candidates confuse schema drift with statistical drift, thinking any change in feature values qualifies as statistical drift, but the key differentiator is that schema drift specifically involves changes in data type or structure, not just distributional shifts.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Schema drift
Schema drift occurs when the structure or data types of features in production data differ from the baseline used during model training. In this scenario, a feature that was an integer in the baseline is now a string in production, which is a classic example of schema drift. SageMaker Model Monitor detects this by comparing the inferred schema of production data against the baseline schema, flagging any type mismatches as constraint violations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Schema drift
Why this is correct
Schema drift occurs when the structure or data types of incoming features diverge from the baseline, such as a string arriving where an integer was expected. This mismatch triggers the constraint violations the monitoring jobs report.
- ✗
Concept drift
Why it's wrong here
Concept drift describes a change in the relationship between features and the target, altering what correct predictions look like. Here the schema itself changed — a string where the baseline held an integer — so the data quality constraint on column type fails, which is data drift.
- ✗
Statistical drift
Why it's wrong here
Statistical drift covers distribution shifts in numeric feature values, detected by metrics such as KL divergence or Jensen-Shannon distance. A column changing from integer to string is a schema or data type mismatch, which Model Monitor flags as a data quality constraint violation, not a statistical one.
- ✗
Bias drift
Why it's wrong here
Bias drift measures shifts in predicted outcomes across demographic groups, not feature data types. SageMaker Model Monitor's data quality baseline compares schema and types; a string replacing an integer triggers a data type constraint violation, which is data drift, not bias drift.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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